Automatic Detection and Characterization of Ground Occlusions in Urban Point Clouds From Mobile Laser Scanning Data

Abstract

Abstract. Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have been classified. The method generates various raster images according to segmented point cloud elements, and detects gaps within the ground based on their connectivity and the application of the hit-or-miss transform. The method has been tested in four real case studies in the cities of Vigo and Paris, and an accuracy of 99.6% has been obtained in occlusion detection and labelling. Cars caused 80.6% of the occlusions. Each car occluded an average ground area of 11.9 m2. The proposed method facilitates knowing the percentage of occluded ground, and if this would be reduced in successive multi-temporal acquisitions based on mobility characteristics of each object class.

Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

“…On the other hand, in conditions of extreme environmental noise (such as that generated by torrential rain or large amounts of dust-sand in suspension), MLS acquisitions are not scheduled or executed. Occlusions, although generated in various parts of the objects, are typically only located in low areas for the tall objects [35], where it was found to have little effect on the misclassification of pole-like objects, trees, or even pedestrians. Completely occluded objects were also not extracted from the urban point cloud for further classification.…”

Detection and reconstruction of static vehicle-related ground occlusions in point clouds from mobile laser scanning

“…Some researchers have discussed the influence of dynamic occlusion caused by vehicles on the localization ability of point cloud data [21,22]. Balado, González, et al [2] discusses the matching method between common environmental objects such as vehicles and ground occlusions caused by them. Compared with the other two kinds of vehicle related occlusions, the research on the vehicle-related ground occlusions is not deep enough.…”

“…In generic point clouds use, accurate geometric and topological information is often an important prerequisite to ensure correct results. Hence additional labor and time costs are needed to detect and reconstruct occlusion before or during data processing [2,3].…”

“…Because of the data acquisition efficiency and cost, most MLS is based on the vehicle in practice. This means the movement trajectory of MLS has to be subject to the constraints of the road network, the traffic regulations, and the prevailing traffic conditions [2]. In addition, the occlusion in MLS data has some other characteristics.…”

A Knowledge-Based Multi-Scale Adaptive Classification Approach for Mobile Laser Scanning Point Clouds in Urban Scenes

“…Recently, many scholars have carried out in-depth research and made some progress in the field of classification for MLS point clouds in urban scenes [20]- [34]. For example, Aijazi et al realized the complex task of classification of 3D urban point clouds by combining two approaches, that is, image converting and the super voxel employing [20].…”